Compiler Engineer

Job details:

Location:

Dubai | Salary:
Competitive Salary | Job Type: Permanent | Discipline: IT & Technology | Reference: 53594 | Posted: 7 days ago

Charterhouse is currently working with a rapidly growing technology innovator at the forefront of AI hardware solutions. Our client specializes in designing and developing cutting‑edge, customized server hardware optimized for artificial intelligence and machine learning applications. Their mission is to accelerate AI initiatives globally by delivering high‑performance, scalable, and energy‑efficient infrastructure. As part of their expansion, they are looking to hire an experienced Compiler Engineer to join their team.

This is an opportunity to work in a dynamic environment where contributions will directly influence next‑generation AI hardware and software ecosystems.

In this role, the Compiler Engineer will design and implement MLIR dialects and lowering pipelines targeting advanced AI accelerator/NPU platforms. The role includes transforming ML graphs from frameworks such as ONNX and PyTorch into optimized kernels with a focus on fusion, tiling, and vectorization. A critical part of the work will involve profiling, benchmarking, and optimizing compiler output to meet stringent latency, throughput, and power targets.

Collaboration with hardware architects and runtime/framework teams will be essential to co‑design compiler features. Additionally, you will build a portable compiler stack that supports multiple hardware generations.

Our client is looking to hire a qualified Engineer with at least five years’ experience working in compiler engineering or high‑performance systems. The candidate should be confident in modern C++ programming and comfortable using Python for scripting and tooling. Hands‑on experience with MLIR or LLVM is key, especially in areas like dialect design, lowering, and optimization. A strong understanding of compiler internals such as intermediate representations, scheduling, vectorization, and loop transformations.

The successful candidate also needs familiarity with major AI/ML frameworks such as PyTorch, ONNX, and Tensor Flow, along with a good grasp of core machine learning operations like tensor manipulation, matrix multiplications, quantization, and handling dynamic shapes. A degree in Computer Science, Computer Engineering, or a related field is required; an advanced degree (M.S. or Ph.D.) is preferred.

A candidate with experience targeting NPU or AI accelerator hardware will be highly valued. Contributions to open‑source compiler projects such as MLIR, LLVM, Torch‑MLIR, or ONNX-MLIR are considered a strong advantage. Familiarity with runtime systems, scheduling, resource sharing, and memory movement engines will be beneficial.